Job Description
Job Description
Role : Sr. Data Scientist / Machine Learning Engineer
Location : South San Francisco, CA (On-site)
Job Type : Long- Term Contract (W2 candidates only)
Job description
Key Accountabilities :
Partner with fellow Data Scientists, ML engineers, MLOps / DevOps engineers and cross functional teams to solve complex problems and create unique solutions by using modern NLP technologies in particular LLMs.
Build data pipelines and deployment pipelines for ML models.
Development of ML models according to business and functional requirements.
Able to help deploy various models and tune them for better performance.
Document and communicate the design and implementation details.
Contribute to the DSE AI team on technical decisions.
Collaborate with clients, informatics departments to deploy scalable and easy-to-maintain solutions.
Serves as a technical point of contact for enterprise wide technologies solutions. Leads complex troubleshooting efforts and root cause analysis.
Qualifications :
Experience with LLM applications development including tool using and reasoning, for instance RAG solution and code interpreter.
Experience with LLM fine tuning a big plus
Experience in building data pipelines and deployment pipelines for LLM applications
Recent experience with ML / AI toolkits such as AWS Sagemager (other toolkits like Pytorch, Tensorflow, Keras, MXNet, H20, etc are nice to have).
Experience with MLOps technologies (Sagemaker, Vertex AI, Kubeflow)
Experience with cloud solutions (AWS / Azure / GCP), docker
Proven scripting and automation skills
Good knowledge of : git, bash, linux, CI / CD tools (e.g. jenkins, gitlab CI), software lifecycle, RDB, visualization tools eg Tableau, Jira, confluence
Programming languages : Python, R
Test driven development, good coding practices
Problem-solving and decision-making skills.
Good interpersonal skills.
Customer & delivery focus.
Ability to work effectively with team members and virtual teams from different locations and different cultural backgrounds.
Experience with deployment of scalable apps a plus
Experience with clinical study data a plus
Education / Years of Experience :
Master in quantitative field (e.g. mathematics, statistics, computer science, EE, etc.), and / or Life Sciences degree with significant computational experience, or equivalent, with 5+ year working experience in Data Science. PhD a plus.
2+ years of commercial Data Engineering / ML Engineering / MLOps / UI / UX engineering experience
3+ years of commercial software engineering experience
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